Author(s)

Tse Sparthan¹, Wolfgang Nzie¹, Bertin Sohfotsing², Tibi Beda³, Olivier Garro¹

¹Department of Mechanical Engineering, National Advance School of Agro-Industrial Science, University of Ngaoundere, Ngaoundere, Cameroon.
²Department of Mechanical Engineering, University Institute of Technology Fotso Victor, University of Dschang, Dschang, Cameroon.
³Department of Physics, Faculty of Science, University of Ngaoundere, Ngaoundere, Cameroon.

In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.

Failure Prediction (FP), Remaining Useful Life (RUL), Artificial Intelligence (AI), Traintrack System, ANFIS, Modeling

Sparthan, T. , Nzie, W. , Sohfotsing, B. , Beda, T. and Garro, O. (2020) A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System. Open Journal of Applied Sciences, 10, 732-757. doi: 10.4236/ojapps.2020.1011052.